Abstract Extending from central California to Alaska, bull kelp (Nereocystis luetkeana) forms seasonal kelp forests that are iconic coastal ecosystems in much of the eastern Pacific. Historical and ongoing field surveys and aerial imagery are used to provide biological data on kelp canopy cover and health, but satellite remote sensing provides the opportunity to generate consistent, long-term datasets over a large spatial scale. Robust satellite-based timeseries measurements of giant kelp (Macrocystis pyrifera) are available for much of the California coastline (e.g., Santa Barbara to Santa Cruz), but there have been no equivalent publications for bull kelp. Recent loss of bull kelp in northern California emphasized the need for more expansive long-term monitoring of canopy trends. We tested various kelp classification approaches using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to Landsat imagery, which allowed sufficient temporal and spatial data collection during bull kelp's narrow seasonal maximum, and compare with the California Department of Fish and Wildlife (CDFW) aerial survey canopy area product. We addressed five main topics that have relevance to enabling Landsat in long-term monitoring of bull kelp canopy coverage in northern California: (1) the effect of MESMA configurations applied to Landsat imagery, including software dependencies and endmember configurations, (2) comparison of Landsat to traditional surveys, (3) differences across Landsat sensors, (4) tidal influence on canopy area, and (5) trends in the decadal timeseries. We found that there was no statistical difference (p = 0.53) between MESMA platforms (IDL-based and a Python-scripted method; RMSE 1.5 km2 or 17.6% when normalized by the range in CDFW), and that a 7-endmember MESMA model provided the lowest RMSE (1.4 km2 or 16.9%). Furthermore, while tidal phases can submerge or emerge kelp canopy and thus potentially affect kelp detection, we found a weak and statistically insignificant correlation between tides and performance of our remote estimate of kelp canopy area. Canopy estimations from Landsat-8 images yielded a higher NRMSE than Landsat-4 and -5 images, but the lack of matchup limits comparison. Imagery from early fall yielded the largest coverage estimates. Overall, our results show that Landsat enables broad remote measurement of bull kelp canopy coverage to supplement existing survey methods and increase continuity of timeseries for monitoring long-term trends.

中文翻译：

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使用 Landsat 绘制加利福尼亚北部的海藻树冠图以实现长期监测

摘要 从加利福尼亚中部延伸到阿拉斯加，公牛海带 (Nereocystis luetkeana) 形成季节性海带森林，是东太平洋大部分地区的标志性沿海生态系统。历史和正在进行的实地调查和航拍图像用于提供有关海带冠层覆盖和健康的生物数据，但卫星遥感提供了在大空间范围内生成一致的长期数据集的机会。加州大部分海岸线（例如，圣巴巴拉到圣克鲁斯）都可以获得基于卫星的巨型海带（Macrocystis pyrifera）的稳健时间序列测量，但没有关于公牛海带的等效出版物。最近加利福尼亚北部海带的消失强调了对冠层趋势进行更广泛的长期监测的必要性。我们使用应用于 Landsat 影像的多端元光谱混合分析 (MESMA) 测试了各种海带分类方法，这允许在公牛海带狭窄的季节性最大值期间收集足够的时间和空间数据，并与加州鱼类和野生动物部 (CDFW) 航空调查进行比较树冠面积产品。我们讨论了与使 Landsat 长期监测加利福尼亚北部海藻冠层覆盖相关的五个主要主题：(1) MESMA 配置应用于 Landsat 影像的影响，包括软件依赖性和端元配置，(2) 比较Landsat 与传统调查，(3) Landsat 传感器之间的差异，(4) 潮汐对冠层面积的影响，以及 (5) 年代际时间序列的趋势。我们发现没有统计学差异（p = 0。53）在 MESMA 平台之间（基于 IDL 和 Python 脚本方法；RMSE 为 1.5 平方公里或 17.6%，当按 CDFW 的范围归一化时），并且 7 端元 MESMA 模型提供最低的 RMSE（1.4 平方公里或 16.9%） . 此外，虽然潮汐阶段可以淹没或出现海带冠层，从而可能影响海带检测，但我们发现潮汐与我们对海带冠层面积的远程估计的性能之间存在微弱且统计上不显着的相关性。Landsat-8 图像的冠层估计产生的 NRMSE 高于 Landsat-4 和 -5 图像，但缺乏匹配限制了比较。初秋的图像产生了最大的覆盖范围估计。全面的，